Machine learning based optimization for vehicle-to-infrastructure communications

Xiang, Wei, Huang, Tao, and Wan, Wanggen (2019) Machine learning based optimization for vehicle-to-infrastructure communications. Future Generation Computer Systems, 94. pp. 488-495.

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In this paper, we study wireless communications in vehicle-to-infrastructure communications. In certain situations, multiple vehicles within a local range need to exchange information via common roadside infrastructure. Example scenarios include busy intersections, and a driver with the knowledge of information from other vehicles can make safer decisions. Fast and reliable communications are essential in such use cases. We consider two different system models in this paper. In the first model, we consider the case where both the base station and vehicles are equipped with a single antenna. In the second model, we discuss the case where multiple antennas are installed on both the base station and vehicles. We show how the system can be optimized in both cases. We then discuss how machine learning can be adopted in both models to realize the optimized system performance. (C) 2018 Elsevier B.V. All rights reserved.

Item ID: 57816
Item Type: Article (Research - C1)
ISSN: 0167-739X
Keywords: Vehicle-to-infrastructure, Internet of vehicles, Single-antenna system, Multi-antenna system, Machine learning, Information exchange
Copyright Information: © 2018 Elsevier B.V. All rights reserved.
Funders: National Natural Science Foundation of China
Projects and Grants: NNSFC grant 61628102
Date Deposited: 03 Apr 2019 07:50
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 50%
40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave) @ 50%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8901 Communication Networks and Services > 890199 Communication Networks and Services not elsewhere classified @ 100%
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